Accurate Boundary Research Articles

Accurate Discrete-Time Modeling and Boundary Analysis for High-Order Wireless Power Transfer Systems

Accurate Discrete-Time Modeling and Boundary Analysis for High-Order Wireless Power Transfer Systems

A theoretical derivation of slip boundary conditions based on the Cercignani–Lampis–Lord scattering model

To characterize fluid flow in the slip regime, the use of Navier–Stokes–Fourier (NSF) equations with slip boundary conditions is prevalent. This trend underscores the necessity of developing reliable and accurate slip boundary conditions. According to kinetic theory, slip behaviours are intrinsically linked to the gas scattering processes at the surface. The widely used Maxwell scattering model, which employs a single accommodation coefficient to describe gas scattering processes, reveals its limitations when the difference between accommodation coefficients in the tangential and normal directions becomes significant. In this work, we provide a derivation of velocity slip and temperature jump boundary conditions based on the Cercignani–Lampis–Lord scattering model, which applies two independent accommodation coefficients to describe the gas scattering process. A Knudsen layer correction term is introduced to account for the impact of the surface on the velocity distribution function, which is associated with the scattering model. The governing equation of the correction term is established based on the linearized Boltzmann equation. Additionally, two moments are derived to capture the collision effect in the Knudsen layer: a conserving moment of collision invariants, and an approximate higher-order conserving moment. These moments are then employed to determine the coefficients in the correction term. We demonstrate that the derived slip coefficients align closely with numerical results obtained by solving the Boltzmann equation in the Knudsen layer. Besides, we apply the derived slip boundary conditions within the framework of the NSF equations, yielding numerical results that exhibit excellent consistency with those obtained through molecular-level simulations.

Physics-informed neural networks for weakly compressible flows using Galerkin–Boltzmann formulation

In this work, we study the Galerkin–Boltzmann formulation within a physics-informed neural network (PINN) framework to solve flow problems in weakly compressible regimes. The Galerkin–Boltzmann equations are discretized with second-order Hermite polynomials in microscopic velocity space, which leads to a first-order conservation law with six equations. Reducing the output dimension makes this equation system particularly well suited for PINNs compared with the widely used D2Q9 lattice Boltzmann velocity space discretizations. We created two distinct neural networks to overcome the scale disparity between the equilibrium and non-equilibrium states in collision terms of the equations. We test the accuracy and performance of the formulation with benchmark problems and solutions for forward and inverse problems with limited data. We compared our approach with the incompressible Navier–Stokes equation and the D2Q9 formulation. We show that the Galerkin–Boltzmann formulation results in similar L2 errors in velocity predictions in a comparable training time with the Navier–Stokes equation and lower training time than the D2Q9 formulation. We also solve forward and inverse problems for a flow over a square, try to capture an accurate boundary layer, and infer the relaxation time parameter using available data from a high-fidelity solver. Our findings show the potential of utilizing the Galerkin–Boltzmann formulation in PINN for weakly compressible flow problems.

Mapping landforms of a hilly landscape using machine learning and high-resolution LiDAR topographic data

Landform maps are important tools in assessment of soil- and eco-hydrogeomorphic processes and hazards, hydrological modeling, and natural resources and land management. Traditional techniques of mapping landforms based on field surveys or from aerial photographs can be time and labor intensive, highlighting the importance of remote sensing products based automatic or semi-automatic approaches. In addition, the time-intensive manual labeling can also be subjective rather than an objective identification of the landform. Here we implemented such an objective approach applying a random forest machine learning algorithm to a set of observed landform data and 1m horizontal resolution bare-earth digital elevation model (DEM) developed from airborne light detection and ranging (LiDAR) data to rapidly map various landforms of a hilly landscape. The landform classification includes upland plateaus, ridges, convex slopes, planar slopes, concave slopes, stream channels, and valley bottoms, across a 400 km2 hilly landscape of the Ozark Mountains in northeastern Oklahoma. We used 4200 landform observations (600 per landform) and eight topographic indices derived from 2 m, 5 m and 10 m resolution LiDAR DEM in random forest algorithm to develop 2 m, 5 m and 10 m resolution landform models. We test the effectiveness of DEM resolution in mapping landforms via comparison of observed landforms with modeled landforms. Results showed that the approach mapped ∼84% of observed landforms when covariates were at 2 m resolution to ∼89% when they were at 10 m resolution. However, predicted maps showed that the 2 m resolution covariates performed better at capturing accurate landform boundaries and details of small-sized landforms such as stream channels and ridges. The approach presented here significantly reduces the time required for mapping landforms compared to traditional techniques using aerial imagery and field observations and allows incorporation of a wide variety of covariates. The landform map developed using this approach has several potential applications. It could be utilized in hydrological modeling, natural resource management, and characterizing soil-geomorphic processes and hazards in a hilly landscape.

Reinforcement Learning with Multi-Policy Movement Strategy for Weakly Supervised Temporal Sentence Grounding

Temporal grounding involves identifying the target moment based on the provided sentence in an untrimmed video. In weakly supervised temporal grounding studies, existing temporal sentence grounding methods face challenges in (1) learning semantic alignment between the candidate window and language query and (2) identifying accurate temporal boundaries during the grounding process. In this work, we propose a reinforcement learning (RL)-based multi-policy movement framework (MMF) for weakly supervised temporal sentence grounding. We imitate the behavior of human beings when grounding specified content in a video, starting from a coarse location and then identifying fine-grained temporal boundaries. The RL-based framework initially sets a series of candidate windows and learns to adjust them step-by-step by maximizing the rewards, indicating the semantic alignment between the current window and the query. To better learn the alignment, we propose a Gaussian-based Dual-Alignment Module (GDAM) which combines the strengths of both scoring-based and reconstruction-based alignment methods, addressing the issues of negative sample bias and language bias. We also employ the multi-policy movement strategy (MMS) which grounds the temporal position in a coarse-to-fine manner. Extensive experiments demonstrate that our proposed method outperforms existing weakly supervised algorithms, achieving state-of-the-art performance on the Charades-STA and ActivityNet Captions datasets.

Road Boundary Extraction Method from Mobile Laser Scanning Point Clouds

Abstract With the rapid development of mobile laser scanning (MLS) technology, high-precision three-dimensional point cloud data has shown great potential in different fields such as topographic mapping, road asset management and smart city construc-tion. Three-dimensional point cloud data contains not only position information, but also the shape and attributes of the target, which is very convenient for obtaining road information. Accurate extraction of the road boundary is a basic task for obtaining road infrastructural data, which can support the generation of high-precision maps, vehicle navigation and auton-omous driving. However, road boundary extraction in urban environments is easily occluded such as vehicles and pedestri-ans on the road, which leads to problems such as difficulty and incomplete extraction of MLS point cloud road boundaries. To address this problem, this study proposes a road boundary extraction method that integrates pavement edge information, accurately considers the position of the road boundary from two dimensions, eliminates false boundaries, and completes the missing boundary through the extracted boundary spatial relationship. First, grid elevation filtering is used to remove high-level non-ground points. Then the pavement edges and curb stone points are extracted from the preprocessed point cloud, and they are superimposed to remove false boundary points to obtain accurate road boundaries. Finally, based on the spatial relationship of road boundaries, missing parts are detected and repaired to obtain complete road boundaries. Experimental results show that the accuracy on real road scenes exceeds 98%, the completeness rate is above 91%, and the extraction quality is above 90%, which verifies the effectiveness and accuracy of this method.

An One-step Triple Enhanced weakly supervised semantic segmentation using image-level labels.

Weakly supervised semantic segmentation, based on image-level labels, abandons the pixel-level labels relied upon by traditional semantic segmentation algorithms. It only utilizes images as supervision information, thereby reducing the time cost and human resources required for marking pixel data. The prevailing approach in weakly supervised segmentation involves two-step method, introducing an additional network and numerous parameters, thereby complicating the model structure. Furthermore, image-level labels typically furnishes only category information for the entire image, lacking specific location details and accurate target boundaries during model training. We propose an innovative One-Step Triple Enhanced weakly supervised semantic segmentation network(OSTE). OSTE streamlines the model structure, which can accomplish both pseudo-labels generation and semantic segmentation tasks in just one step. Furthermore, we augment the weakly supervised semantic segmentation network in three key aspects based on the class activation map construction method, thereby enhancing segmentation accuracy: Firstly, by integrating local information from the activation map with the image, we can enhance the network's localization and expansion capabilities to obtain more accurate and rich location information. Then, we refine the seed areas of the class activation map by exploiting the correlation between multi-level feature. Finally, we incorporate conditional random field theory to generate pseudo-labels with higher confidence and richer boundary information. In comparison to the prevailing two-step weakly supervised semantic segmentation schemes, the segmentation network proposed in this paper achieves a more competitive mean Intersection over Union (mIoU) score of 58.47% on Pascal VOC. Additionally, it enhances the mIoU score by at least 5.03% when compared to existing end-to-end schemes.

Study on Atomization Mechanism of Oil Injection Lubrication for Rolling Bearing Based on Stratified Method

The atomization mechanism of lubrication fluid in rolling bearings under high-speed airflow between the rings was investigated. A simulation model of gas–liquid two-phase flow in angular contact ball bearings was developed, and the jet lubrication process between the bearing rings was simulated using FLUENT computational fluid dynamics software (Ansys 19.2). The complex motion boundary conditions of the rolling elements were addressed through a layered approach. We can obtain more accurate boundary layer flow field changes and statistics of the diameter of oil particles in lubricating oil atomization, which lays the foundation for analyzing the law of influence on lubricating oil atomization. The results show that as the number of boundary layer layers increases, the influence of the boundary layer flow field on the lubricating oil is more obvious. The oil particle size is excessively flat, and the concentration of large particles of oil appears to decrease. As the speed increases, the amount of oil in the cavity decreases, but the oil droplets are also fragmented, which intensifies the atomization and reduces the particle diameter. This reduces the Sauter Mean Diameter (SMD), which is not conducive to the lubrication of the bearing. Under different injection pressures, when the injection pressure is large, it is beneficial to the lubrication of the bearing.

Instability flow mechanism of ultra-high head pumped-storage units during turbine runaway process

Ultra-high head pumped-storage units (PSUs) have longer upstream and downstream pipelines and more complex internal flows than those of conventional head units. This complexity increases the difficulty of simulating transient flows in ultra-high head pump-turbines (PTs). When numerically studying the transition process of an ultra-high head PSU, determining accurate time-varying boundary conditions and analyzing complex pressure pulsations is crucial. In this study, one- and three-dimensional (1D-3D) coupled computational approach was employed to simulate the turbine runaway process (TRP). The short-time Fourier transform (STFT) method was used to analyze the time–frequency characteristics of the transient pressure, revealing the formation mechanism of each pressure pulsation component. In addition to the pressure pulsation components that occur during the TRP in conventional head PTs, a novel pressure pulsation component was revealed. This component, extended from low frequency to high frequency and had a higher amplitude at 25 ∼ 27 times the rotational frequency, was induced by transient flowrate pulsations. This could significantly exacerbate the pulsation of the axial force. The findings provide a reference for the subsequent research and development of ultra-high head PSUs.

BioSAM: Generating SAM Prompts From Superpixel Graph for Biological Instance Segmentation.

Proposal-free instance segmentation methods have significantly advanced the field of biological image analysis. Recently, the Segment Anything Model (SAM) has shown an extraordinary ability to handle challenging instance boundaries. However, directly applying SAM to biological images that contain instances with complex morphologies and dense distributions fails to yield satisfactory results. In this work, we propose BioSAM, a new biological instance segmentation framework generating SAM prompts from a superpixel graph. Specifically, to avoid over-merging, we first generate sufficient superpixels as graph nodes and construct an initialized graph. We then generate initial prompts from each superpixel and aggregate them through a graph neural network (GNN) by predicting the relationship of superpixels to avoid over-segmentation. We employ the SAM encoder embeddings and the SAM-assisted superpixel similarity as new features for the graph to enhance its discrimination capability. With the graph-based prompt aggregation, we utilize the aggregated prompts in SAM to refine the segmentation and generate more accurate instance boundaries. Comprehensive experiments on four representative biological datasets demonstrate that our proposed method outperforms state-of-the-art methods.

Validation of an eigenvalue-based inverse method for estimating locally-reacting surface impedance

Accurate boundary condition descriptions are essential for creating accurate computational models for room acoustics. These descriptions are often defined in terms of absorption coefficients or surface impedances. However, obtaining complex-valued, frequency-dependent values for an acoustically absorbing material sample at low frequencies is a challenging task. In fact, the current measurement standard ISO 354:2003 does not provide a method for measuring absorption coefficients below 100 Hz. In this paper, a recently proposed eigenvalue-based inverse method for estimating locally-reacting impedance at measurement system resonance frequencies is validated. The validation uses data from three samples measured in an impedance tube. The method is also used to estimate complex-valued, frequency-dependent impedances at frequencies below 100 Hz in a measured reverberant room and in a simulated reverberation chamber.

FBSM: Foveabox-based boundary-aware segmentation method for green apples in natural orchards

Image segmentation of target fruits is an essential part of machine vision systems, aiming to facilitate more accurate early fruit measurement and machine harvesting in natural orchard environments. Given that instance-level pooling and down-sampling operations in conventional segmentation models often lead to the loss of detailed information, resulting in coarse partitioning masks, we endeavor to restore the boundary information of the masks. To achieve high-quality fruit segmentation with clear boundaries and refined masks, a Foveabox-based boundary-aware segmentation model (FBSM) is constructed by adding a multi-stage mask prediction head incorporating fine-grained features to the anchor-free detection model FoveaBox to realize accurate segmentation of green apple fruits. At each stage, a bi-layer fusion structure (BFS) is employed to fully fuse the fine-grained features in a double-fusion manner to guide the subsequent instance mask prediction. Finally, the boundary recovery module (BRM) is leveraged to recover the lost boundary detail information and obtain more accurate boundaries for the continuous optimization process of the fruit instance mask. Experimental results demonstrate that the proposed FBSM model achieves a mean average precision (mAP) of 60.7 % for green apple instance segmentation in unstructured natural orchard environments, surpassing traditional instance segmentation models. Moreover, it enhances base model detection accuracy by 1.3 % while striking a better balance between detection and segmentation performance.

Accurate backside boundary recognition of girth weld beads

Visual recognition of weld beads is essential for post-weld robotic grinding. The recognition of thin-walled weld bead boundary, especially the backside boundary, remains challenging due to the diverse features such as debris, misalignment, and deformation. Based on point cloud from a laser scanner, we present a robust and accurate backside boundary recognition method for girth weld beads of thin-walled pipes. A boundary point extraction method is designed based on an adaptive sliding window model. Without prior morphology features, the influence of misalignment and deformation on the accuracy of boundary point recognition is greatly reduced by the local model matching strategy. Leveraging the correlation among overall weld bead features, an anomalous boundary point recognition and correction method based on DBSCAN clustering is proposed to further enhance robustness. A series of validation experiments were conducted by the obtained backside point cloud data inside a girth weld pipe, and our proposed method showed a high accuracy and a high robustness to misalignment, deformation and debris features.

Prediction of directional solidification in freeze casting of biomaterial scaffolds using physics-informed neural networks

Freeze casting, a manufacturing technique widely applied in biomedical fields for fabricating biomaterial scaffolds, poses challenges for predicting directional solidification due to its highly nonlinear behavior and complex interplay of process parameters. Conventional numerical methods, such as computational fluid dynamics (CFD), require adequate and accurate boundary condition knowledge, limiting their utility in real-world transient solidification applications due to technical limitations. In this study, we address this challenge by developing a physics-informed neural networks (PINNs) model to predict directional solidification in freeze-casting processes. The PINNs model integrates physical constraints with neural network predictions, requiring significantly fewer predetermined boundary conditions compared to CFD. Through a comparison with CFD simulations, the PINNs model demonstrates comparable accuracy in predicting temperature distribution and solidification patterns. This promising model achieves such a performance with only 5000 data points in space and time, equivalent to 250,000 timesteps, showcasing its ability to predict solidification dynamics with high accuracy. The study’s major contributions lie in providing insights into solidification patterns during freeze-casting scaffold fabrication, facilitating the design of biomaterial scaffolds with finely tuned microstructures essential for various tissue engineering applications. Furthermore, the reduced computational demands of the PINNs model offer potential cost and time savings in scaffold fabrication, promising advancements in biomedical engineering research and development.

An improved numerical model of high-speed friction stir welding using a new tool-workpiece slip ratio

The interface slip state of high-speed friction stir welding (HSFSW) is challenging to determine, and accurate boundary conditions cannot be obtained. First, this paper studies the effects of velocity and temperature on the interface slip state, and establishes a new slip coefficient equation. Then, this equation was used to modify the velocity boundary conditions and improve the numerical calculation method of HSFSW. A numerical model of HSFSW was established and validated through experimental data. Finally, the effects of traverse speed and rotational speed on the temperature and void defects of HSFSW were elaborated, and the applicable process parameter range was determined. Specifically, void defects were observed at a rotational speed of 6000 rpm with traverse speeds of 1000 mm/min and 2000 mm/min, while surface peeling occurred at a traverse speed of 3000 mm/min with a rotational speed of 7000 rpm. This study establishes the optimal parameter range for aluminum alloy HSFSW, providing valuable guidance for future industrial applications.

Tribofilm distribution and tribological analysis of piston ring-cylinder liner interfaces under realistic engine conditions

The study investigates tribofilm distribution and tribology performance on piston ring-cylinder liner surfaces under realistic internal combustion (IC) engine conditions. By examining the effects of load, temperature, and oil film thickness ratio, the research reveals that the tribofilm is significantly greater on the cylinder liner surface compared to the piston ring surface. The findings highlight that a specific excitation pressure is necessary for tribofilm formation. An oil film thickness ratio of 1.8041 is evaluated as an accurate boundary for tribofilm distribution on the cylinder liner surface. These insights provide valuable data for designing piston ring-cylinder liner interfaces and improving IC engine performance.

Coupled building simulation and CFD for real-time window and HVAC control in sports space

Accurately predicting players’ thermal comfort in large sports spaces for real-time window and HVAC operations presents significant challenges due to non-uniform thermal distributions. Standalone building energy simulation (BES) typically assumes the entire targeted space as a uniform thermal zone, which fails to capture these variances. Conversely, Computational Fluid Dynamics (CFD) simulation can predict indoor thermal environments with precision but often struggles with determining accurate boundary conditions. This study introduces a coupled BES and CFD simulation method tailored for real-time window and HVAC control in sports spaces. A case study was conducted in a prototypical national fitness hall to evaluate the effectiveness of the proposed method. The thermal comfort and cooling energy results from the co-simulation-based control were compared with those from standalone EnergyPlus simulation-based control and fixed-schedule window and HVAC operations, which served as baselines. The results indicate that the proposed method enhanced thermal comfort by 68.5 % compared to constantly-scheduled window operations and achieved daily energy savings of up to 43.5 % versus constantly-scheduled HVAC operations. Furthermore, significant discrepancies in Average Predicted Mean Vote (PMV‾) or Average Adaptive Predicted Mean Vote (aPMV‾) as well as daily cooling energy consumption between the BES-CFD co-simulation and standalone EnergyPlus simulation were identified, ranging from -0.53 to 1.83 for PMV‾ (aPMV‾) and -2444.6 to 1266.5 kWh for cooling energy. This study contributes novel methods for real-time window and HVAC control in sports buildings towards thermally comfortable and energy-efficient sports environments.

CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation.

Some cognitive research has discovered that humans accomplish event segmentation as a side effect of event anticipation. Inspired by this discovery, we propose a simple yet effective end-to-end self-supervised learning framework for event segmentation/boundary detection. Unlike the mainstream clustering-based methods, our framework exploits a transformer-based feature reconstruction scheme to detect event boundaries by reconstruction errors. This is consistent with the fact that humans spot new events by leveraging the deviation between their prediction and what is perceived. Thanks to their heterogeneity in semantics, the frames at boundaries are difficult to be reconstructed (generally with large reconstruction errors), which is favorable for event boundary detection. In addition, since the reconstruction occurs on the semantic feature level instead of the pixel level, we develop a temporal contrastive feature embedding (TCFE) module to learn the semantic visual representation for frame feature reconstruction (FFR). This procedure is like humans building up experiences with "long-term memory." The goal of our work is to segment generic events rather than localize some specific ones. We focus on achieving accurate event boundaries. As a result, we adopt the F1 score (Precision/Recall) as our primary evaluation metric for a fair comparison with previous approaches. Meanwhile, we also calculate the conventional frame-based mean over frames (MoF) and intersection over union (IoU) metric. We thoroughly benchmark our work on four publicly available datasets and demonstrate much better results. The source code is available at https://github.com/wang3702/CoSeg.

A magnetic diagnostic suite for the Pegasus-III experiment.

Pegasus-III is an ultralow aspect ratio spherical tokamak providing a dedicated US experiment for comparative solenoid-free startup studies. A new magnetic diagnostic suite for equilibrium and low frequency (<200kHz) magnetohydrodynamic mode analysis has been installed. These new diagnostics address the significant challenges of measuring magnetic field in a high noise environment with the majority constrained to fit in an 8mm diagnostic gap on the high field side. Electrostatic switching noise generated by the 16 independent current feedback-controlled power supplies produces dVcm/dt ∼ 1kV/μs and volt level common mode noise on the magnetics. Immunity to this switching noise is accomplished through differential signal runs and signal processing, along with end-to-end electromagnetic interference shielding. The magnetic measurements are simultaneously digitized at 1MHz and conditioned by precision 8 pole Butterworth filters with a corner frequency of 200kHz to prevent aliasing down to the 16-bit level over the full passband. Ex-vessel calibrations of the Bp coils were completed with a typical uncertainty of <0.5%. Stray toroidal field pickup from coil misalignment or positioning errors is corrected using a physics-based model. Comparisons of the corrected measurements to modeling agree to within 1.3% on average. This is within the 1.5% measurement uncertainty that a sensitivity analysis determined is needed for accurate fast boundary and equilibrium reconstruction.

Identifying long-term burned forests in the rugged terrain of Southwest China:A novel method based on remote sensing and ecological mechanisms

Burned forests were detected using remote sensing techniques. Yet, identifying long-term burned forests in the mountains, especially at a large spatial extent, remains a great challenge due to a lack of long-term high-resolution remote sensing data or the unsatisfactory performance of the moderate-resolution remotely sensed data in complex mountain landscapes. Efficient, robust, and cost-effective methods are urgently called for. In this study, we developed a novel method combining remote sensing and ecological mechanisms (short for RSEM-M) with a paired-pixel approach to identify the long-term burned forests in Southwest China, where the terrain is rugged. In mapping burned forests in 2010, the overall accuracy, producer accuracy, user accuracy, Kappa coefficient, and field validation accuracy were 92.27 %, 95.86 %, 88.36 %, 0.85, and 81.16 %, respectively. Compared to the standard procedure and FireCCI51 and MCD64A1 fire products, the RSEM-M shows higher accuracy and consistency in identifying burned forests of different sizes, with fewer invalid pixels, better spatial continuity, and more accurate boundary delineation. Then, using the RSEM-M, we mapped burned forests in Southwest China from 2002 to 2017. The cumulative areas were 37.69 × 104 ha. The RSEM-M improved the efficiency and accuracy of long-term burned forest identifications in mountainous landscapes at a large spatial extent.